Generalizing to Unseen Domains in Diabetic Retinopathy Classification

Document Type

Conference Proceeding

Publication Title

Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024

Abstract

Diabetic retinopathy (DR) is caused by long-standing diabetes and is among the fifth leading cause for visual impairment. The prospects of early diagnosis and treatment could be helpful in curing the disease, however, the detection procedure is rather challenging and mostly tedious. Therefore, automated diabetic retinopathy classification using deep learning techniques has gained interest in the medical imaging community. Akin to several other real-world applications of deep learning, the typical assumption of i.i.d data is also violated in DR classification that relies on deep learning. Therefore, developing DR classification methods robust to unseen distributions is of great value. In this paper, we study the problem of generalizing a model to unseen distributions or domains (a.k.a domain generalization) in DR classification. To this end, we propose a simple and effective domain generalization (DG) approach that achieves self-distillation in vision transformers (ViT) via a novel prediction softening mechanism. This prediction softening is an adaptive convex combination of one-hot labels with the model's own knowledge. We perform extensive experiments on challenging open-source DR classification datasets under both multi-source and more challenging single-source DG settings with three different ViT backbones to establish the efficacy and applicability of our approach against competing methods. For the first time, we report the performance of several state-of-the-art domain generalization (DG) methods on open-source DR classification datasets after conducting thorough experiments. Finally, our method is also capable of delivering improved calibration performance than other methods, showing its suitability for safety-critical applications, including health-care. We hope that our contributions would instigate more DG research across the medical imaging community. Code is available at github.com/Chumsy0725/SPSD-ViT.

First Page

7670

Last Page

7680

DOI

10.1109/WACV57701.2024.00751

Publication Date

1-1-2024

Keywords

Algorithms, and algorithms, Applications, Biomedical / healthcare / medicine, formulations, Machine learning architectures

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